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63e3bc6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 | import torch
import gradio as gr
import numpy as np
from PIL import Image
from model import ConditionalVAE
CHECKPOINT_PATH = "best_model_inference.pt"
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
DEFAULT_CLASS_NAMES = ["akiec", "bcc", "bkl", "df", "mel", "nv", "vasc"]
CLASS_DESCRIPTIONS = {
"akiec": "AKIEC — Queratosis actínica / enfermedad de Bowen",
"bcc": "BCC — Carcinoma basocelular",
"bkl": "BKL — Lesión benigna tipo queratosis",
"df": "DF — Dermatofibroma",
"mel": "MEL — Melanoma",
"nv": "NV — Nevus melanocítico",
"vasc": "VASC — Lesión vascular",
}
def load_cvae():
ckpt = torch.load(CHECKPOINT_PATH, map_location=DEVICE, weights_only=False)
class_names = ckpt.get("class_names", DEFAULT_CLASS_NAMES)
args = ckpt.get("args", {})
latent_dim = int(args.get("latent_dim", 128))
beta = float(args.get("beta", 1.0))
model = ConditionalVAE(
latent_dim=latent_dim,
num_classes=len(class_names),
beta=beta,
).to(DEVICE)
model.load_state_dict(ckpt["model"])
model.eval()
return model, class_names, ckpt
model, class_names, ckpt = load_cvae()
def tensor_to_pil(img_tensor):
"""
img_tensor: Tensor (3, H, W) en [0, 1]
"""
img = img_tensor.detach().cpu().clamp(0, 1)
img = img.permute(1, 2, 0).numpy()
img = (img * 255).astype(np.uint8)
return Image.fromarray(img)
@torch.no_grad()
def generate_images(class_name, temperature, n_images):
class_idx = class_names.index(class_name)
imgs = model.generate(
class_label=class_idx,
n=int(n_images),
device=DEVICE,
temperature=float(temperature),
)
return [tensor_to_pil(imgs[i]) for i in range(imgs.size(0))]
def class_info(class_name):
return CLASS_DESCRIPTIONS.get(class_name, class_name)
description = f"""
# CVAE — Generación sintética de lesiones de piel
Este demo genera imágenes sintéticas de lesiones dermatológicas usando un Conditional VAE entrenado sobre 7 clases.
**Modelo:** Conditional Variational Autoencoder
**Checkpoint epoch:** {ckpt.get("epoch", "N/A")}
**Best val loss:** {ckpt.get("best_val", "N/A")}
**Clases:** {", ".join(class_names)}
⚠️ Demo académico. No usar para diagnóstico médico.
"""
with gr.Blocks(title="Skin Lesion CVAE") as demo:
gr.Markdown(description)
with gr.Row():
with gr.Column():
class_name = gr.Dropdown(
choices=class_names,
value=class_names[0],
label="Clase de lesión",
)
class_description = gr.Textbox(
value=class_info(class_names[0]),
label="Descripción",
interactive=False,
)
temperature = gr.Slider(
minimum=0.2,
maximum=1.5,
value=0.8,
step=0.1,
label="Temperatura",
)
n_images = gr.Slider(
minimum=1,
maximum=8,
value=4,
step=1,
label="Número de imágenes",
)
btn = gr.Button("Generar imágenes")
with gr.Column():
gallery = gr.Gallery(
label="Imágenes sintéticas generadas",
columns=4,
height="auto",
)
class_name.change(
fn=class_info,
inputs=class_name,
outputs=class_description,
)
btn.click(
fn=generate_images,
inputs=[class_name, temperature, n_images],
outputs=gallery,
)
if __name__ == "__main__":
demo.launch()
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